mirror of
https://github.com/wassname/ray.git
synced 2026-07-15 11:25:40 +08:00
Sam ToyerandEric Liang
663e92ab3f
* [rllib] Separate optimisers for DDPG actor & crit. * [rllib] Better names for DDPG variables & options Config changes: - noise_scale -> exploration_ou_noise_scale - exploration_theta -> exploration_ou_theta - exploration_sigma -> exploration_ou_sigma - act_noise -> exploration_gaussian_sigma - noise_clip -> target_noise_clip * [rllib] Make DDPG less class-y Used functions to replace three classes with only an __init__ method & a handful of unrelated attributes. * [rllib] Refactor DDPG noise * [rllib] Unify DDPG exploration annealing Added option "exploration_should_anneal" to enable linear annealing of exploration noise. By default this is off, for consistency with DDPG & TD3 papers. Also renamed "exploration_final_eps" to "exploration_final_scale" (that name seems to have been carried over from DQN, and doesn't really make sense here). Finally, tried to rename "eps" to "noise_scale" wherever possible.
141 lines
3.1 KiB
Python
141 lines
3.1 KiB
Python
"""Registry of algorithm names for `rllib train --run=<alg_name>`"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import traceback
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from ray.rllib.contrib.registry import CONTRIBUTED_ALGORITHMS
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def _import_appo():
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from ray.rllib.agents import ppo
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return ppo.APPOTrainer
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def _import_qmix():
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from ray.rllib.agents import qmix
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return qmix.QMixTrainer
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def _import_apex_qmix():
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from ray.rllib.agents import qmix
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return qmix.ApexQMixTrainer
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def _import_ddpg():
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from ray.rllib.agents import ddpg
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return ddpg.DDPGTrainer
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def _import_apex_ddpg():
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from ray.rllib.agents import ddpg
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return ddpg.ApexDDPGTrainer
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def _import_td3():
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from ray.rllib.agents import ddpg
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return ddpg.TD3Trainer
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def _import_ppo():
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from ray.rllib.agents import ppo
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return ppo.PPOTrainer
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def _import_es():
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from ray.rllib.agents import es
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return es.ESTrainer
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def _import_ars():
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from ray.rllib.agents import ars
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return ars.ARSTrainer
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def _import_dqn():
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from ray.rllib.agents import dqn
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return dqn.DQNTrainer
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def _import_apex():
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from ray.rllib.agents import dqn
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return dqn.ApexTrainer
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def _import_a3c():
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from ray.rllib.agents import a3c
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return a3c.A3CTrainer
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def _import_a2c():
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from ray.rllib.agents import a3c
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return a3c.A2CTrainer
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def _import_pg():
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from ray.rllib.agents import pg
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return pg.PGTrainer
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def _import_impala():
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from ray.rllib.agents import impala
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return impala.ImpalaTrainer
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def _import_marwil():
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from ray.rllib.agents import marwil
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return marwil.MARWILTrainer
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ALGORITHMS = {
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"DDPG": _import_ddpg,
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"APEX_DDPG": _import_apex_ddpg,
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"TD3": _import_td3,
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"PPO": _import_ppo,
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"ES": _import_es,
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"ARS": _import_ars,
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"DQN": _import_dqn,
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"APEX": _import_apex,
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"A3C": _import_a3c,
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"A2C": _import_a2c,
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"PG": _import_pg,
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"IMPALA": _import_impala,
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"QMIX": _import_qmix,
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"APEX_QMIX": _import_apex_qmix,
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"APPO": _import_appo,
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"MARWIL": _import_marwil,
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}
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def get_agent_class(alg):
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"""Returns the class of a known agent given its name."""
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try:
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return _get_agent_class(alg)
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except ImportError:
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from ray.rllib.agents.mock import _agent_import_failed
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return _agent_import_failed(traceback.format_exc())
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def _get_agent_class(alg):
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if alg in ALGORITHMS:
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return ALGORITHMS[alg]()
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elif alg in CONTRIBUTED_ALGORITHMS:
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return CONTRIBUTED_ALGORITHMS[alg]()
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elif alg == "script":
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from ray.tune import script_runner
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return script_runner.ScriptRunner
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elif alg == "__fake":
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from ray.rllib.agents.mock import _MockTrainer
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return _MockTrainer
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elif alg == "__sigmoid_fake_data":
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from ray.rllib.agents.mock import _SigmoidFakeData
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return _SigmoidFakeData
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elif alg == "__parameter_tuning":
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from ray.rllib.agents.mock import _ParameterTuningTrainer
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return _ParameterTuningTrainer
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else:
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raise Exception(("Unknown algorithm {}.").format(alg))
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